A Semi-Supervised Learning Approach for UWB Ranging Error Mitigation
Tianyu Wang, Keke Hu, Zhihang Li, Kangbo Lin, Jian Wang, Yuan Shen
Abstract
Non-line-of-sight (NLOS) propagation conditions can severely degrade wireless localization accuracy due to the biases in range measurements. Machine learning methods such as support vector machine (SVM) can mitigate the effect of NLOS biases when sufficient labeled ranging measurements are available. This letter proposes a semi-supervised learning approach for NLOS identification and mitigation, which leverages low-cost unlabeled measurements by self-training to complement only a small portion of labeled ones. Experimental results show that the proposed semi-supervised approach can increase the NLOS identification probability from 90% to 94% and reduce the ranging error by 10% by exploiting the unlabeled measurements.